HDGC-hybrid task offloading framework using deep reinforcement learning and genetic algorithms for 6G edge cloud

With the continuous integration of IoT technology and information technology, edge computing, as an emerging computing paradigm, makes full use of terminals to process and analyse real-time data. The explosion of Internet of Things (IoT) devices has created challenges for traditional cloud-based data processing models due to high latency and availability requirements. This paper proposes a new edge computation-based framework for iot data processing and scheduling using deep reinforcement learning. The system architecture incorporates distributed iot data access, realtime processing, and an intelligent scheduler based on Deep q networks (DQN). A large number of experiments show that compared with traditional scheduling methods, the average task completion time is reduced by 20% and resource utilization is increased by 15%. The unique integration of edge computing and deep reinforcement learning provides a flexible and efficient platform for lowlatency iot applications. Key results obtained from testing the proposed system, such as reduced task completion time and increased resource utilization.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Due to the exponential expansion of the Internet of Things (IoT) technology, the increasing amount of data generated by numerous IoT devices has become a significant challenge for communication and computing networks. On the other hand, the concept of Mobile Edge Computing (MEC) presents a viable solution as it brings computing, storage, and networking capabilities in close proximity to the user, enabling the hosting of applications that require significant computation power and low latency right at the network’s edge. In addition, the maneuverability, cost-effectiveness, and ease of deployment make Unmanned Aerial Vehicles (UAVs) highly versatile wireless platforms. Capitalizing on the benefits of MEC and UAVs, this paper proposes a UAV-assisted MEC system that offloads services to reduce the computation burden in IoT. The objective is to maximize the task completion rate, considering factors such as user transmit power, task offloading rate, and UAV trajectory variables. To tackle this optimization problem, this paper devises a method based on the Deep Deterministic Policy Gradient (DDPG), an algorithm for continuous action spaces in deep reinforcement learning. The numerical simulations show the effectiveness of the proposed approach in comparison to other benchmarks, i.e., deep Q-network (DQN) and full offloading methods. In a simulation, the proposed DDPG method can achieve a fully optimized system with a 100% task success rate, while the DQN-based method achieves a lower task success rate of 95.93% and the full offloading method only obtains a task success rate of 82.73%.

Task offloading in mobile edge computing (MEC) becomes particularly challenging when dealing with complex, interdependent subtasks, especially under dynamic network conditions and resource constraints. This challenge is central to enabling intelligent and adaptive behavior in holographic counterparts, real-time virtual replicas of physical consumer Internet of Things (IoT) devices such as smart wearables, home automation units, or augmented reality systems. In this paper, we propose HiDeR-GQ, a novel Hierarchical Dependency Reduction framework that integrates quantized graph-based deep reinforcement learning (DRL) which acts as the decision-making core of holographic counterparts deployed in heterogeneous edge networks. The core of HiDeR-GQ is a customized deep reinforcement learning agent that integrates a quantized Graph Attention Network (GATv2) for structure-aware state encoding of task dependency graphs, and a personalized federated Double Deep Q-Network (DDQN) to enable collaborative learning across distributed edge nodes while accounting for heterogeneity. To further optimize performance, we introduce a latency-aware dependency pruning mechanism that hierarchically simplifies task graphs by removing non-critical dependencies to reduce end-to-end latency without compromising task accuracy. We evaluate HiDeR-GQ in a simulated MEC environment involving multiple edge servers and users executing directed acyclic graph (DAG) structured application tasks. The results demonstrate that HiDeR-GQ achieves up to 25% lower average task completion delay, higher cumulative reward, and reduced communication cost compared to state-of-the-art offloading strategies. These benefits come with minimal trade-offs in decision optimality, highlighting HiDeR-GQ’s efficiency and scalability for next-generation IoT and edge intelligence applications.

Multi-agent deep reinforcement learning for task offloading in group distributed manufacturing systems

Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy-, and infrastructure-efficient communications. Each IoT device transmits its own information by backscattering the signals from the primary network without using active radio-frequency (RF) transmitter chain. We consider the symbiosis between the cellular network and the IoT network and focus on the user association problem in SRN. Specifically, the base station (BS) in the primary network serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem. To overcome this issue, we propose two deep reinforcement learning (DRL) algorithms, both use historical information to infer the current information in order to make appropriate decisions. One algorithm, referred to as centralized DRL, makes decisions for all IoT devices at one time with globally available information. The other algorithm, referred to as distributed DRL, makes a decision only for one IoT device at one time using locally available information. Finally, simulation results show that the two proposed DRL algorithms achieve performance comparable to the optimal user association policy which requires perfect real-time information, and the distributed DRL algorithm has the advantage of scalability.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

With the rapid advancement of autonomous driving technology, the increasing computational demands of intelligent vehicles have driven the adoption of cloud and edge computing to augment limited onboard resources. However, this cloud–edge integration presents new challenges for efficient task offloading. In addition, the high mobility of vehicles further complicates the design of reliable offloading strategies. To address these challenges, this paper proposes a Cloud-Edge-Vehicle (CEV) framework that leverages predictable vehicle trajectories for optimized task offloading. A spatio-temporal multi-head self-attention long short-term memory (ST-MHSA LSTM) model is designed to accurately predict vehicle trajectories by capturing motion trends and interactions with neighboring vehicles. Building upon the trajectory prediction information, a deep reinforcement learning (DRL)-based task offloading algorithm is proposed. This algorithm incorporates a dynamic priority assignment strategy to prioritize delay-sensitive tasks according to their urgency, thereby improving offloading performance and reducing system costs associated with task transmission and execution. To mitigate the adverse effects of inevitable trajectory prediction errors, a prediction consistency-based deviation correction strategy is further introduced, enhancing decision robustness in dynamic scenarios. Simulation results show that as task numbers increase, the proposed framework outperforms traditional methods (i.e., local, edge, cloud, and random computing) in task success ratio, processing delay, and energy consumption. Task success ratio improves by 115.79%, 114.44%, 27.09%, and 135.19% over local, edge, cloud, and random computing, respectively. Average processing delay is reduced by 70.47%, 62.23%, 38.69%, and 79.85%, while average energy consumption decreases by 65.33%, 66.59%, 59.12%, and 74.66%. These results highlight the framework’s superior performance for computation-intensive and delay-sensitive vehicular applications.

The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT network. There has been limited research on resource management strategies in fog networks in recent years, and a limited systematic review has been done to compile these studies. This article focuses on current developments in resource allocation strategies for fog-IoT networks. A systematic review of resource allocation techniques with the key objective of enhancing QoS is provided. Steps involved in conducting this systematic literature review include developing research goals, accessing studies, categorizing and critically analysing the studies. The resource management approaches engaged in this article are load balancing and task offloading techniques. For the load balancing approach, a brief survey of recent work done according to their sub-categories, including stochastic, probabilistic/statistic, graph theory and hybrid techniques is provided whereas for task offloading, the survey is performed according to the destination of task offloading. Efficient load balancing and task-offloading approaches contribute significantly to resource management, and tremendous effort has been put into this critical topic. Thus, this survey presents an overview of these extents and a comparative analysis. Finally, the study discusses ongoing research issues and potential future directions for developing effective management resource allocation techniques.

UAV-assisted task offloading for IoT in smart buildings and environment via deep reinforcement learning

In recent years, deep reinforcement learning (DRL) has made impressive achievements in many fields. However, existing DRL algorithms usually require a large amount of exploration to obtain a good action policy. In addition, in many complex situations, the reward function cannot be well designed to meet task requirements. These two problems will make it difficult for DRL to learn a good action policy within a relatively short period. The use of expert data can provide effective guidance and avoid unnecessary exploration. This study proposes a deep imitation reinforcement learning (DIRL) algorithm that uses a certain amount of expert demonstration data to speed up the training of DRL. In the proposed method, the learning agent imitates the expert's action policy by learning from demonstration data. After imitation learning, DRL is used to optimise the action policy in a self‐learning way. By experimental comparison on a video game called the Mario racing game, it is shown that the proposed DIRL algorithm with expert demonstration data can obtain much better performance than previous DRL algorithms without expert guidance.

Point of Interest (POI) recommendation is one of the important means for businesses to fully understand user preferences and meet their personalized needs, laying a solid foundation for the development of e-commerce and social networks. However, traditional social network POI recommendation algorithms suffer from various problems such as low accuracy and low recall. Therefore, a social network POI recommendation algorithm using the Internet of Things (IoT) and deep reinforcement learning (DRL) is proposed. First, the overall framework of the POI recommendation algorithm is designed by integrating IoT technology and DRL algorithm. Second, under the support of this framework, IoT technology is utilized to deeply explore users' personalized preferences for POI recommendation, analyze the internal rules of user check-in behavior and integrate multiple data sources. Finally, a DRL algorithm is used to construct the recommendation model. Multiple data sources are used as input to the model, based on which the check-in probability is calculated to generate the POI recommendation list and complete the design of the social network POI recommendation algorithm. Experimental results show that the accuracy of the proposed algorithm for social network POI recommendation has a maximum value of 98%, the maximum recall is 97% and the root mean square error is low. The recommendation time is short, and the maximum recommendation quality is 0.92, indicating that the recommendation effect of the proposed algorithm is better. By applying this method to the e-commerce field, businesses can fully utilize POI recommendation to recommend products and services that are suitable for users, thus promoting the development of the social economy.

With the increasing use of Internet of Things (IoT) devices in various industries, the challenge of efficient use of energy and reducing the amount of device downtime is a key concern in sustainable and reliable systems usage. The paper deals with energy consumption optimization and the forecasting of device failures in the IoT systems in industrial automation. More precisely, we suggest the hybrid solution consisting of reinforcement learning (RL) to optimize energy consumption, Long Short-Memory (LSTM) networks to predictive maintenance, and edge-cloud offloading solutions to improve the performance of devices and their sustainability. We use the modeling of heterogeneous IoT devices in our methodology, but with different energy profiles, different workloads, and operational modes. We use RL to dynamically tweak devices modes, which saves energy and does not impact task completion. We combine predictive maintenance, in which we use LSTM to forecast device failures using historical data to give timely warnings on maintenance. Moreover, the edge-cloud offloading decisions are also integrated into our system to further optimize the energy consumption and optimize the efficiency of performing tasks. The principal results are that our method is highly energy efficient and device uptime, and reinforcement learning is the most energy saving method. The predictive maintenance model is an effective way of cutting on the downtime, as the correct prediction of the occurrence of failure results in the taking of preventive measures. Task offloading, especially when there are resource constraints, improves system reliability without affecting the performance. Such findings indicate that adaptive IoT systems could be used to optimize energy use and system performance and, therefore, make industrial automation more sustainable. Our solution is informative to the design of future IoT systems in which energy efficiency and predictable stability will be the most crucial attributes. Keywords: IoT Energy Optimization, Predictive Maintenance, Reinforcement Learning, LSTM Networks, Edge-Cloud Offloading.

In modern mobile edge computing (MEC) systems, effectively integrating service caching, task offloading, and service delivery is crucial for ensuring high network quality and enhanced user experience. In this article, we introduce a novel joint optimization framework that employs Uncrewed aerial vehicles (UAVs) as dynamic edge computing nodes, collaborating with service collection units (SCUs) that aggregate service requests from Internet of Things (IoT) devices. In the proposed system, UAVs equipped with small-capacity edge servers can process computational tasks locally or forward them to a base station (BS) via the backhaul when necessary. Both user layers (UEs) and UAVs dynamically cache programs using an adaptive algorithm based on task frequency and program weight, which maximizes cache hit rates and expedites service execution. A joint optimization problem is formulated to minimize the overall end-to-end latency through coordinated task offloading, CPU allocation, caching, and UAV scheduling, subject to energy, storage, and mobility constraints. Our resource-aware task offloading and UAV trajectory planning further enable efficient service responsiveness and minimize reliance on the backhaul link. The overall objective is to reduce maximum task latency and backhaul congestion across the network. Extensive simulation results show that our framework significantly outperforms standard baselines, including random caching, most caching, and calculation delay and energy consumption multiobjective optimization problem (CDECMOP), achieving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.5\%$</tex-math> </inline-formula> lower energy consumption and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$23.6\%$</tex-math> </inline-formula> lower latency across diverse network scenarios. These findings underscore the effectiveness of the proposed UE-UAV cooperative caching and computation strategy for scalable, energy-efficient, and low-latency UAV-assisted MEC systems.